Project Description:
Recent advances in distributed fibre sensing have shown that existing optical fibre cables, originally deployed for telecommunications, can also serve as dense sensor arrays. By sending probe signals through the fibre and analysing the backscattered light, it is possible to extract information about environmental changes such as vibrations, temperature, and strain along the cable path. One of the most promising applications of this technology is in maritime environments, where fibre sensing can detect the acoustic and vibrational signatures generated by vessels.
Goal:
The goal of this project is to develop and train algorithms that can process data from fibre sensing systems and identify vessel types with high accuracy. Particular focus will be placed on feature extraction from noisy distributed acoustic sensing (DAS) data, supervised and unsupervised classification techniques, and the integration of contextual data such as AIS (Automatic Identification System) feeds for validation. Experimental data from deployed fibre cables in real-world scenarios will be available to support algorithm development and testing.